Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations1486
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory197.5 KiB
Average record size in memory136.1 B

Variable types

Categorical7
Numeric6
Text4

Alerts

book_out_of_library_days is highly overall correlated with returned_lateHigh correlation
book_pages is highly overall correlated with book_priceHigh correlation
book_price is highly overall correlated with book_pagesHigh correlation
customer_age is highly overall correlated with customer_age_groupHigh correlation
customer_age_group is highly overall correlated with customer_ageHigh correlation
library_address is highly overall correlated with library_nameHigh correlation
library_name is highly overall correlated with library_addressHigh correlation
returned_late is highly overall correlated with book_out_of_library_daysHigh correlation
customer_gender is uniformly distributed Uniform

Reproduction

Analysis started2024-10-23 14:35:49.455529
Analysis finished2024-10-23 14:36:02.577469
Duration13.12 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

library_name
Categorical

High correlation 

Distinct17
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
Multnomah County Library
164 
Multnomah County Library Sellwood Moreland
 
93
Multnomah County Library Woodstock
 
93
Multnomah County Library Holgate
 
90
Multnomah County Library Albina
 
88
Other values (12)
958 

Length

Max length42
Median length39
Mean length34.063257
Min length24

Characters and Unicode

Total characters50618
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMultnomah County Library Woodstock
2nd rowMultnomah County Library
3rd rowMultnomah County Library Kenton
4th rowMultnomah County Library North Portland
5th rowMultnomah County Library

Common Values

ValueCountFrequency (%)
Multnomah County Library 164
 
11.0%
Multnomah County Library Sellwood Moreland 93
 
6.3%
Multnomah County Library Woodstock 93
 
6.3%
Multnomah County Library Holgate 90
 
6.1%
Multnomah County Library Albina 88
 
5.9%
Multnomah County Library North Portland 86
 
5.8%
Friends of the Multnomah County Library 83
 
5.6%
Multnomah County Library Kenton 83
 
5.6%
Multnomah County Library Hollywood Library 83
 
5.6%
Multnomah County Library Gregory Heights 82
 
5.5%
Other values (7) 541
36.4%

Length

2024-10-23T16:36:02.775465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
library 1569
24.4%
multnomah 1486
23.1%
county 1486
23.1%
sellwood 93
 
1.4%
moreland 93
 
1.4%
woodstock 93
 
1.4%
holgate 90
 
1.4%
albina 88
 
1.4%
north 86
 
1.3%
portland 86
 
1.3%
Other values (16) 1268
19.7%

Most occurring characters

ValueCountFrequency (%)
4952
 
9.8%
o 4590
 
9.1%
t 4035
 
8.0%
r 3809
 
7.5%
n 3794
 
7.5%
a 3725
 
7.4%
y 3220
 
6.4%
u 2972
 
5.9%
l 2888
 
5.7%
i 2130
 
4.2%
Other values (26) 14503
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50618
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4952
 
9.8%
o 4590
 
9.1%
t 4035
 
8.0%
r 3809
 
7.5%
n 3794
 
7.5%
a 3725
 
7.4%
y 3220
 
6.4%
u 2972
 
5.9%
l 2888
 
5.7%
i 2130
 
4.2%
Other values (26) 14503
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50618
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4952
 
9.8%
o 4590
 
9.1%
t 4035
 
8.0%
r 3809
 
7.5%
n 3794
 
7.5%
a 3725
 
7.4%
y 3220
 
6.4%
u 2972
 
5.9%
l 2888
 
5.7%
i 2130
 
4.2%
Other values (26) 14503
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50618
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4952
 
9.8%
o 4590
 
9.1%
t 4035
 
8.0%
r 3809
 
7.5%
n 3794
 
7.5%
a 3725
 
7.4%
y 3220
 
6.4%
u 2972
 
5.9%
l 2888
 
5.7%
i 2130
 
4.2%
Other values (26) 14503
28.7%

library_address
Categorical

High correlation 

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
6008 SE 49th Ave, Portland, OR 97206
 
93
7860 SE 13th Ave, Portland, OR 97202
 
93
7905 SE Holgate Blvd, Portland, OR 97206
 
90
3605 NE 15th Ave, Portland, OR 97212
 
88
512 N Killingsworth St, Portland, OR 97217
 
86
Other values (13)
1036 

Length

Max length47
Median length41
Mean length38.066622
Min length34

Characters and Unicode

Total characters56567
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6008 SE 49th Ave, Portland, OR 97206
2nd row205 NE Russell St, Portland, OR 97212
3rd row8226 N Denver Ave, Portland, OR 97217
4th row512 N Killingsworth St, Portland, OR 97217
5th row205 NE Russell St, Portland, OR 97212

Common Values

ValueCountFrequency (%)
6008 SE 49th Ave, Portland, OR 97206 93
 
6.3%
7860 SE 13th Ave, Portland, OR 97202 93
 
6.3%
7905 SE Holgate Blvd, Portland, OR 97206 90
 
6.1%
3605 NE 15th Ave, Portland, OR 97212 88
 
5.9%
512 N Killingsworth St, Portland, OR 97217 86
 
5.8%
216 NE Knott St, Portland, OR 97212 84
 
5.7%
522 SW 5th Ave, Portland, OR 97204 83
 
5.6%
4040 NE Tillamook St, Portland, OR 97212 83
 
5.6%
8226 N Denver Ave, Portland, OR 97217 83
 
5.6%
7921 NE Sandy Blvd, Portland, OR 97213 82
 
5.5%
Other values (8) 621
41.8%

Length

2024-10-23T16:36:02.963553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
portland 1486
 
14.1%
or 1486
 
14.1%
ave 674
 
6.4%
se 429
 
4.1%
ne 417
 
4.0%
st 414
 
3.9%
97212 335
 
3.2%
blvd 325
 
3.1%
sw 315
 
3.0%
n 244
 
2.3%
Other values (52) 4421
41.9%

Most occurring characters

ValueCountFrequency (%)
9060
 
16.0%
2 2975
 
5.3%
, 2972
 
5.3%
t 2908
 
5.1%
l 2547
 
4.5%
n 2139
 
3.8%
a 2114
 
3.7%
7 2068
 
3.7%
o 2060
 
3.6%
d 1974
 
3.5%
Other values (36) 25750
45.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56567
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9060
 
16.0%
2 2975
 
5.3%
, 2972
 
5.3%
t 2908
 
5.1%
l 2547
 
4.5%
n 2139
 
3.8%
a 2114
 
3.7%
7 2068
 
3.7%
o 2060
 
3.6%
d 1974
 
3.5%
Other values (36) 25750
45.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56567
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9060
 
16.0%
2 2975
 
5.3%
, 2972
 
5.3%
t 2908
 
5.1%
l 2547
 
4.5%
n 2139
 
3.8%
a 2114
 
3.7%
7 2068
 
3.7%
o 2060
 
3.6%
d 1974
 
3.5%
Other values (36) 25750
45.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56567
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9060
 
16.0%
2 2975
 
5.3%
, 2972
 
5.3%
t 2908
 
5.1%
l 2547
 
4.5%
n 2139
 
3.8%
a 2114
 
3.7%
7 2068
 
3.7%
o 2060
 
3.6%
d 1974
 
3.5%
Other values (36) 25750
45.5%

customer_gender
Categorical

Uniform 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
female
743 
male
743 

Length

Max length6
Median length5
Mean length5
Min length4

Characters and Unicode

Total characters7430
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowmale
3rd rowmale
4th rowmale
5th rowfemale

Common Values

ValueCountFrequency (%)
female 743
50.0%
male 743
50.0%

Length

2024-10-23T16:36:03.123466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-23T16:36:03.247858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
female 743
50.0%
male 743
50.0%

Most occurring characters

ValueCountFrequency (%)
e 2229
30.0%
m 1486
20.0%
a 1486
20.0%
l 1486
20.0%
f 743
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7430
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2229
30.0%
m 1486
20.0%
a 1486
20.0%
l 1486
20.0%
f 743
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7430
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2229
30.0%
m 1486
20.0%
a 1486
20.0%
l 1486
20.0%
f 743
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7430
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2229
30.0%
m 1486
20.0%
a 1486
20.0%
l 1486
20.0%
f 743
 
10.0%
Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
High School
376 
Others
350 
College
349 
Graduate Degree
334 
Unknown
77 

Length

Max length15
Median length11
Mean length9.5746972
Min length6

Characters and Unicode

Total characters14228
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduate Degree
2nd rowGraduate Degree
3rd rowGraduate Degree
4th rowHigh School
5th rowCollege

Common Values

ValueCountFrequency (%)
High School 376
25.3%
Others 350
23.6%
College 349
23.5%
Graduate Degree 334
22.5%
Unknown 77
 
5.2%

Length

2024-10-23T16:36:03.412895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-23T16:36:03.590855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
high 376
17.1%
school 376
17.1%
others 350
15.9%
college 349
15.9%
graduate 334
15.2%
degree 334
15.2%
unknown 77
 
3.5%

Most occurring characters

ValueCountFrequency (%)
e 2384
16.8%
o 1178
 
8.3%
h 1102
 
7.7%
l 1074
 
7.5%
g 1059
 
7.4%
r 1018
 
7.2%
710
 
5.0%
t 684
 
4.8%
a 668
 
4.7%
i 376
 
2.6%
Other values (14) 3975
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14228
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2384
16.8%
o 1178
 
8.3%
h 1102
 
7.7%
l 1074
 
7.5%
g 1059
 
7.4%
r 1018
 
7.2%
710
 
5.0%
t 684
 
4.8%
a 668
 
4.7%
i 376
 
2.6%
Other values (14) 3975
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14228
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2384
16.8%
o 1178
 
8.3%
h 1102
 
7.7%
l 1074
 
7.5%
g 1059
 
7.4%
r 1018
 
7.2%
710
 
5.0%
t 684
 
4.8%
a 668
 
4.7%
i 376
 
2.6%
Other values (14) 3975
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14228
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2384
16.8%
o 1178
 
8.3%
h 1102
 
7.7%
l 1074
 
7.5%
g 1059
 
7.4%
r 1018
 
7.2%
710
 
5.0%
t 684
 
4.8%
a 668
 
4.7%
i 376
 
2.6%
Other values (14) 3975
27.9%
Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
Admin & Support
216 
Others
210 
Education & Health
202 
Sales
201 
Tech
199 
Other values (3)
458 

Length

Max length18
Median length11
Mean length10.779273
Min length4

Characters and Unicode

Total characters16018
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTech
2nd rowEducation & Health
3rd rowEducation & Health
4th rowBusiness & Finance
5th rowTech

Common Values

ValueCountFrequency (%)
Admin & Support 216
14.5%
Others 210
14.1%
Education & Health 202
13.6%
Sales 201
13.5%
Tech 199
13.4%
Business & Finance 193
13.0%
Blue Collar 188
12.7%
Unknown 77
 
5.2%

Length

2024-10-23T16:36:03.741854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-23T16:36:03.874854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
611
21.1%
admin 216
 
7.5%
support 216
 
7.5%
others 210
 
7.3%
education 202
 
7.0%
health 202
 
7.0%
sales 201
 
6.9%
tech 199
 
6.9%
business 193
 
6.7%
finance 193
 
6.7%
Other values (3) 453
15.6%

Most occurring characters

ValueCountFrequency (%)
1410
 
8.8%
e 1386
 
8.7%
n 1228
 
7.7%
s 990
 
6.2%
a 986
 
6.2%
l 967
 
6.0%
t 830
 
5.2%
i 804
 
5.0%
u 799
 
5.0%
o 683
 
4.3%
Other values (19) 5935
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16018
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1410
 
8.8%
e 1386
 
8.7%
n 1228
 
7.7%
s 990
 
6.2%
a 986
 
6.2%
l 967
 
6.0%
t 830
 
5.2%
i 804
 
5.0%
u 799
 
5.0%
o 683
 
4.3%
Other values (19) 5935
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16018
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1410
 
8.8%
e 1386
 
8.7%
n 1228
 
7.7%
s 990
 
6.2%
a 986
 
6.2%
l 967
 
6.0%
t 830
 
5.2%
i 804
 
5.0%
u 799
 
5.0%
o 683
 
4.3%
Other values (19) 5935
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16018
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1410
 
8.8%
e 1386
 
8.7%
n 1228
 
7.7%
s 990
 
6.2%
a 986
 
6.2%
l 967
 
6.0%
t 830
 
5.2%
i 804
 
5.0%
u 799
 
5.0%
o 683
 
4.3%
Other values (19) 5935
37.1%

customer_age
Real number (ℝ)

High correlation 

Distinct61
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.860027
Minimum8
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-10-23T16:36:04.040257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile11
Q123
median38
Q353
95-th percentile65
Maximum68
Range60
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.249035
Coefficient of variation (CV)0.45560018
Kurtosis-1.1777336
Mean37.860027
Median Absolute Deviation (MAD)15
Skewness-0.016156124
Sum56260
Variance297.52922
MonotonicityNot monotonic
2024-10-23T16:36:04.179231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 37
 
2.5%
55 33
 
2.2%
52 33
 
2.2%
58 31
 
2.1%
30 30
 
2.0%
41 30
 
2.0%
19 30
 
2.0%
27 29
 
2.0%
61 29
 
2.0%
67 29
 
2.0%
Other values (51) 1175
79.1%
ValueCountFrequency (%)
8 21
1.4%
9 22
1.5%
10 25
1.7%
11 23
1.5%
12 24
1.6%
13 27
1.8%
14 27
1.8%
15 22
1.5%
16 21
1.4%
17 29
2.0%
ValueCountFrequency (%)
68 14
0.9%
67 29
2.0%
66 25
1.7%
65 15
1.0%
64 16
1.1%
63 22
1.5%
62 21
1.4%
61 29
2.0%
60 22
1.5%
59 20
1.3%

customer_age_group
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
35-60
638 
18-35
414 
Under 18
241 
60+
193 

Length

Max length8
Median length5
Mean length5.2267833
Min length3

Characters and Unicode

Total characters7767
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row35-60
2nd row35-60
3rd row18-35
4th row35-60
5th row35-60

Common Values

ValueCountFrequency (%)
35-60 638
42.9%
18-35 414
27.9%
Under 18 241
 
16.2%
60+ 193
 
13.0%

Length

2024-10-23T16:36:04.417266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-23T16:36:04.519236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
35-60 638
36.9%
18-35 414
24.0%
under 241
 
14.0%
18 241
 
14.0%
60 193
 
11.2%

Most occurring characters

ValueCountFrequency (%)
3 1052
13.5%
5 1052
13.5%
- 1052
13.5%
6 831
10.7%
0 831
10.7%
1 655
8.4%
8 655
8.4%
U 241
 
3.1%
n 241
 
3.1%
d 241
 
3.1%
Other values (4) 916
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7767
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1052
13.5%
5 1052
13.5%
- 1052
13.5%
6 831
10.7%
0 831
10.7%
1 655
8.4%
8 655
8.4%
U 241
 
3.1%
n 241
 
3.1%
d 241
 
3.1%
Other values (4) 916
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7767
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1052
13.5%
5 1052
13.5%
- 1052
13.5%
6 831
10.7%
0 831
10.7%
1 655
8.4%
8 655
8.4%
U 241
 
3.1%
n 241
 
3.1%
d 241
 
3.1%
Other values (4) 916
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7767
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1052
13.5%
5 1052
13.5%
- 1052
13.5%
6 831
10.7%
0 831
10.7%
1 655
8.4%
8 655
8.4%
U 241
 
3.1%
n 241
 
3.1%
d 241
 
3.1%
Other values (4) 916
11.8%
Distinct1411
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
2024-10-23T16:36:04.908990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length54
Median length50
Mean length37.242934
Min length23

Characters and Unicode

Total characters55343
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1385 ?
Unique (%)93.2%

Sample

1st row4942 SE 28th Ave, Portland, OR 97202
2nd row2821 NE Klickitat St, Portland, OR 97212
3rd row6614 N Wilbur Ave, Portland, OR 97217
4th row2617 NE 33rd Ave, Portland, OR 97212
5th row5425 SE Lafayette St, Portland, OR 97206
ValueCountFrequency (%)
or 1449
 
13.9%
portland 1279
 
12.3%
ave 565
 
5.4%
se 416
 
4.0%
st 410
 
3.9%
ne 400
 
3.8%
sw 262
 
2.5%
n 228
 
2.2%
97217 134
 
1.3%
dr 125
 
1.2%
Other values (2083) 5158
49.5%
2024-10-23T16:36:05.374171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8940
 
16.2%
, 2879
 
5.2%
2 2693
 
4.9%
t 2549
 
4.6%
r 2183
 
3.9%
a 2147
 
3.9%
7 2144
 
3.9%
9 2049
 
3.7%
n 2026
 
3.7%
o 1942
 
3.5%
Other values (53) 25791
46.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55343
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8940
 
16.2%
, 2879
 
5.2%
2 2693
 
4.9%
t 2549
 
4.6%
r 2183
 
3.9%
a 2147
 
3.9%
7 2144
 
3.9%
9 2049
 
3.7%
n 2026
 
3.7%
o 1942
 
3.5%
Other values (53) 25791
46.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55343
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8940
 
16.2%
, 2879
 
5.2%
2 2693
 
4.9%
t 2549
 
4.6%
r 2183
 
3.9%
a 2147
 
3.9%
7 2144
 
3.9%
9 2049
 
3.7%
n 2026
 
3.7%
o 1942
 
3.5%
Other values (53) 25791
46.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55343
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8940
 
16.2%
, 2879
 
5.2%
2 2693
 
4.9%
t 2549
 
4.6%
r 2183
 
3.9%
a 2147
 
3.9%
7 2144
 
3.9%
9 2049
 
3.7%
n 2026
 
3.7%
o 1942
 
3.5%
Other values (53) 25791
46.6%
Distinct1346
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8021225
Minimum0.168
Maximum19.055
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-10-23T16:36:05.495165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.168
5-th percentile0.73625
Q12.371
median4.2885
Q36.36675
95-th percentile12.09925
Maximum19.055
Range18.887
Interquartile range (IQR)3.99575

Descriptive statistics

Standard deviation3.2089687
Coefficient of variation (CV)0.66823965
Kurtosis1.725872
Mean4.8021225
Median Absolute Deviation (MAD)1.992
Skewness1.2062656
Sum7135.954
Variance10.29748
MonotonicityNot monotonic
2024-10-23T16:36:05.619175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.932 10
 
0.7%
4.165 8
 
0.5%
7.143 4
 
0.3%
2.211 3
 
0.2%
3.456 3
 
0.2%
0.695 3
 
0.2%
2.156 3
 
0.2%
6.69 3
 
0.2%
4.558 3
 
0.2%
2.911 3
 
0.2%
Other values (1336) 1443
97.1%
ValueCountFrequency (%)
0.168 1
0.1%
0.218 1
0.1%
0.427 1
0.1%
0.429 1
0.1%
0.44 1
0.1%
0.443 1
0.1%
0.47 1
0.1%
0.471 1
0.1%
0.488 1
0.1%
0.494 1
0.1%
ValueCountFrequency (%)
19.055 1
0.1%
16.948 1
0.1%
16.927 1
0.1%
16.777 1
0.1%
16.49 1
0.1%
16.126 1
0.1%
15.792 2
0.1%
15.684 1
0.1%
15.658 1
0.1%
15.645 1
0.1%
Distinct203
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
2024-10-23T16:36:05.971796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length200
Median length70
Mean length40.154105
Min length7

Characters and Unicode

Total characters59669
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowIndian Financial System 5E
2nd rowAdvertising Management
3rd rowGeostatistics for Natural Resources Evaluation
4th rowHighlights of Natural Resources Management
5th rowSome Vocational Resources and Needs of a Rural Community as Determined by the Location of Special Groups
ValueCountFrequency (%)
of 628
 
7.6%
and 477
 
5.8%
the 427
 
5.2%
medicine 270
 
3.3%
resources 250
 
3.0%
financial 247
 
3.0%
advertising 227
 
2.8%
mechanics 217
 
2.6%
engines 165
 
2.0%
in 109
 
1.3%
Other values (481) 5194
63.3%
2024-10-23T16:36:06.518092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6725
 
11.3%
e 5432
 
9.1%
n 5137
 
8.6%
i 4812
 
8.1%
a 4032
 
6.8%
o 3572
 
6.0%
t 3268
 
5.5%
r 2972
 
5.0%
s 2809
 
4.7%
c 2700
 
4.5%
Other values (64) 18210
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 59669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6725
 
11.3%
e 5432
 
9.1%
n 5137
 
8.6%
i 4812
 
8.1%
a 4032
 
6.8%
o 3572
 
6.0%
t 3268
 
5.5%
r 2972
 
5.0%
s 2809
 
4.7%
c 2700
 
4.5%
Other values (64) 18210
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 59669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6725
 
11.3%
e 5432
 
9.1%
n 5137
 
8.6%
i 4812
 
8.1%
a 4032
 
6.8%
o 3572
 
6.0%
t 3268
 
5.5%
r 2972
 
5.0%
s 2809
 
4.7%
c 2700
 
4.5%
Other values (64) 18210
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 59669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6725
 
11.3%
e 5432
 
9.1%
n 5137
 
8.6%
i 4812
 
8.1%
a 4032
 
6.8%
o 3572
 
6.0%
t 3268
 
5.5%
r 2972
 
5.0%
s 2809
 
4.7%
c 2700
 
4.5%
Other values (64) 18210
30.5%
Distinct158
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
2024-10-23T16:36:06.831093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length162
Median length94
Mean length26.536339
Min length4

Characters and Unicode

Total characters39433
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowKhan
2nd rowC. L. Tyagi, Arun Kumar
3rd rowPierre Goovaerts, Department of Civil and Environmental Engineering Pierre Goovaerts
4th rowUnited States. National Park Service
5th rowEdwin Ray Hoskins
ValueCountFrequency (%)
unknown 414
 
7.2%
of 155
 
2.7%
united 111
 
1.9%
states 111
 
1.9%
and 90
 
1.6%
on 67
 
1.2%
committee 67
 
1.2%
resources 66
 
1.2%
congress 60
 
1.0%
william 59
 
1.0%
Other values (507) 4526
79.0%
2024-10-23T16:36:07.336203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4240
 
10.8%
n 3607
 
9.1%
e 3291
 
8.3%
a 2615
 
6.6%
o 2387
 
6.1%
r 2162
 
5.5%
i 2066
 
5.2%
t 1696
 
4.3%
s 1617
 
4.1%
l 1278
 
3.2%
Other values (57) 14474
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39433
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4240
 
10.8%
n 3607
 
9.1%
e 3291
 
8.3%
a 2615
 
6.6%
o 2387
 
6.1%
r 2162
 
5.5%
i 2066
 
5.2%
t 1696
 
4.3%
s 1617
 
4.1%
l 1278
 
3.2%
Other values (57) 14474
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39433
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4240
 
10.8%
n 3607
 
9.1%
e 3291
 
8.3%
a 2615
 
6.6%
o 2387
 
6.1%
r 2162
 
5.5%
i 2066
 
5.2%
t 1696
 
4.3%
s 1617
 
4.1%
l 1278
 
3.2%
Other values (57) 14474
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39433
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4240
 
10.8%
n 3607
 
9.1%
e 3291
 
8.3%
a 2615
 
6.6%
o 2387
 
6.1%
r 2162
 
5.5%
i 2066
 
5.2%
t 1696
 
4.3%
s 1617
 
4.1%
l 1278
 
3.2%
Other values (57) 14474
36.7%
Distinct87
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
2024-10-23T16:36:07.595182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length41
Median length30
Mean length13.795424
Min length3

Characters and Unicode

Total characters20500
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowAdvertising
3rd rowScience
4th rowConservation of natural resources
5th rowAgricultural education
ValueCountFrequency (%)
235
 
9.0%
unknown 212
 
8.1%
business 199
 
7.6%
economics 193
 
7.4%
medicine 138
 
5.3%
science 95
 
3.7%
advertising 85
 
3.3%
resources 77
 
3.0%
mechanics 66
 
2.5%
and 41
 
1.6%
Other values (114) 1261
48.5%
2024-10-23T16:36:07.987176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 2475
12.1%
e 2035
 
9.9%
i 1980
 
9.7%
s 1685
 
8.2%
c 1359
 
6.6%
o 1321
 
6.4%
1116
 
5.4%
r 922
 
4.5%
a 913
 
4.5%
t 824
 
4.0%
Other values (39) 5870
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2475
12.1%
e 2035
 
9.9%
i 1980
 
9.7%
s 1685
 
8.2%
c 1359
 
6.6%
o 1321
 
6.4%
1116
 
5.4%
r 922
 
4.5%
a 913
 
4.5%
t 824
 
4.0%
Other values (39) 5870
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2475
12.1%
e 2035
 
9.9%
i 1980
 
9.7%
s 1685
 
8.2%
c 1359
 
6.6%
o 1321
 
6.4%
1116
 
5.4%
r 922
 
4.5%
a 913
 
4.5%
t 824
 
4.0%
Other values (39) 5870
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2475
12.1%
e 2035
 
9.9%
i 1980
 
9.7%
s 1685
 
8.2%
c 1359
 
6.6%
o 1321
 
6.4%
1116
 
5.4%
r 922
 
4.5%
a 913
 
4.5%
t 824
 
4.0%
Other values (39) 5870
28.6%

book_price
Real number (ℝ)

High correlation 

Distinct207
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307.2632
Minimum5.99
Maximum721
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-10-23T16:36:08.117542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5.99
5-th percentile39.24
Q1182.5
median309
Q3411
95-th percentile626.99
Maximum721
Range715.01
Interquartile range (IQR)228.5

Descriptive statistics

Standard deviation161.97079
Coefficient of variation (CV)0.52714023
Kurtosis-0.4115207
Mean307.2632
Median Absolute Deviation (MAD)110.99
Skewness0.2382362
Sum456593.12
Variance26234.538
MonotonicityNot monotonic
2024-10-23T16:36:08.256901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 21
 
1.4%
400.99 18
 
1.2%
419.99 16
 
1.1%
284 16
 
1.1%
348.99 15
 
1.0%
414.5 15
 
1.0%
436.99 13
 
0.9%
182.5 13
 
0.9%
311.99 12
 
0.8%
630.5 12
 
0.8%
Other values (197) 1335
89.8%
ValueCountFrequency (%)
5.99 1
 
0.1%
11.5 11
0.7%
14 21
1.4%
21.5 7
 
0.5%
25.5 7
 
0.5%
28 9
0.6%
31 9
0.6%
35.99 4
 
0.3%
38.99 6
 
0.4%
39.99 10
0.7%
ValueCountFrequency (%)
721 8
0.5%
707.99 9
0.6%
654.5 10
0.7%
650.99 9
0.6%
648 10
0.7%
647.99 10
0.7%
630.5 12
0.8%
626.99 8
0.5%
622.5 3
 
0.2%
603.5 6
0.4%

book_pages
Real number (ℝ)

High correlation 

Distinct183
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean590.0148
Minimum124
Maximum1154
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-10-23T16:36:08.412900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum124
5-th percentile327
Q1467
median583
Q3709
95-th percentile910
Maximum1154
Range1030
Interquartile range (IQR)242

Descriptive statistics

Standard deviation173.03646
Coefficient of variation (CV)0.29327477
Kurtosis-0.076497061
Mean590.0148
Median Absolute Deviation (MAD)118
Skewness0.27737883
Sum876762
Variance29941.616
MonotonicityNot monotonic
2024-10-23T16:36:08.570988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
635 23
 
1.5%
720 22
 
1.5%
424 21
 
1.4%
527 20
 
1.3%
327 19
 
1.3%
669 18
 
1.2%
617 18
 
1.2%
408 18
 
1.2%
382 18
 
1.2%
545 17
 
1.1%
Other values (173) 1292
86.9%
ValueCountFrequency (%)
124 5
 
0.3%
202 6
 
0.4%
236 11
0.7%
277 7
 
0.5%
286 5
 
0.3%
305 8
0.5%
320 10
0.7%
322 5
 
0.3%
327 19
1.3%
329 3
 
0.2%
ValueCountFrequency (%)
1154 9
0.6%
967 12
0.8%
932 12
0.8%
931 12
0.8%
927 9
0.6%
925 8
0.5%
918 10
0.7%
910 8
0.5%
900 5
0.3%
893 8
0.5%

book_age
Real number (ℝ)

Distinct109
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.503365
Minimum0
Maximum189
Zeros7
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-10-23T16:36:08.710985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q114
median39.5
Q3105
95-th percentile149
Maximum189
Range189
Interquartile range (IQR)91

Descriptive statistics

Standard deviation51.199113
Coefficient of variation (CV)0.86044063
Kurtosis-1.0387568
Mean59.503365
Median Absolute Deviation (MAD)33.5
Skewness0.5641801
Sum88422
Variance2621.3491
MonotonicityNot monotonic
2024-10-23T16:36:08.848982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 64
 
4.3%
11 44
 
3.0%
96 42
 
2.8%
1 40
 
2.7%
9 38
 
2.6%
3 38
 
2.6%
29 36
 
2.4%
34 36
 
2.4%
5 33
 
2.2%
15 33
 
2.2%
Other values (99) 1082
72.8%
ValueCountFrequency (%)
0 7
 
0.5%
1 40
2.7%
2 18
1.2%
3 38
2.6%
4 11
 
0.7%
5 33
2.2%
6 30
2.0%
7 21
1.4%
8 29
2.0%
9 38
2.6%
ValueCountFrequency (%)
189 1
 
0.1%
188 8
0.5%
168 2
 
0.1%
167 7
0.5%
166 5
0.3%
165 6
0.4%
164 4
0.3%
163 5
0.3%
161 5
0.3%
160 7
0.5%

book_out_of_library_days
Real number (ℝ)

High correlation 

Distinct185
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.29677
Minimum2
Maximum320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-10-23T16:36:08.991919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q112
median19
Q327
95-th percentile159
Maximum320
Range318
Interquartile range (IQR)15

Descriptive statistics

Standard deviation51.667769
Coefficient of variation (CV)1.3148096
Kurtosis5.4927464
Mean39.29677
Median Absolute Deviation (MAD)8
Skewness2.3550475
Sum58395
Variance2669.5583
MonotonicityNot monotonic
2024-10-23T16:36:09.298900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 62
 
4.2%
11 62
 
4.2%
8 61
 
4.1%
16 59
 
4.0%
5 57
 
3.8%
25 55
 
3.7%
6 54
 
3.6%
18 53
 
3.6%
13 53
 
3.6%
20 51
 
3.4%
Other values (175) 919
61.8%
ValueCountFrequency (%)
2 3
 
0.2%
4 1
 
0.1%
5 57
3.8%
6 54
3.6%
7 41
2.8%
8 61
4.1%
9 45
3.0%
10 30
2.0%
11 62
4.2%
12 43
2.9%
ValueCountFrequency (%)
320 1
 
0.1%
302 1
 
0.1%
295 3
0.2%
282 1
 
0.1%
275 1
 
0.1%
270 1
 
0.1%
269 1
 
0.1%
268 1
 
0.1%
261 1
 
0.1%
260 1
 
0.1%

returned_late
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
0
1139 
1
347 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1486
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1139
76.6%
1 347
 
23.4%

Length

2024-10-23T16:36:09.896461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-23T16:36:10.068462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1139
76.6%
1 347
 
23.4%

Most occurring characters

ValueCountFrequency (%)
0 1139
76.6%
1 347
 
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1486
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1139
76.6%
1 347
 
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1486
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1139
76.6%
1 347
 
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1486
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1139
76.6%
1 347
 
23.4%

Interactions

2024-10-23T16:36:01.215466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:55.361912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:56.818913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:57.811912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:58.957912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:36:00.250467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:36:01.358467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:55.652911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:56.977913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:57.946913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:59.134914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:36:00.438468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:36:01.492466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:55.916914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:57.148921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:58.151911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:59.323915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:36:00.632466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:36:01.634472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:56.103911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:57.347911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:58.374911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:59.577912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:36:00.782466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:36:01.761467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:56.353912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:57.500913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:58.565911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:59.786468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:36:00.916467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:36:01.884467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:56.640912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:57.662911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:35:58.773912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:36:00.062465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T16:36:01.080467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-23T16:36:10.202802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
book_agebook_out_of_library_daysbook_pagesbook_pricecustomer_agecustomer_age_groupcustomer_educationcustomer_gendercustomer_library_distance_kmcustomer_occupationlibrary_addresslibrary_namereturned_late
book_age1.000-0.023-0.155-0.119-0.0380.0200.0000.000-0.0390.0000.0000.0000.000
book_out_of_library_days-0.0231.0000.0400.0360.0170.0350.0460.1010.1860.0390.0000.0000.978
book_pages-0.1550.0401.0000.739-0.0270.0170.0000.066-0.0080.0000.0000.0000.108
book_price-0.1190.0360.7391.0000.0010.0000.0340.032-0.0200.0000.0000.0000.034
customer_age-0.0380.017-0.0270.0011.0000.8930.0250.000-0.0020.0330.0000.0000.000
customer_age_group0.0200.0350.0170.0000.8931.0000.0000.0440.0000.0280.0600.0610.030
customer_education0.0000.0460.0000.0340.0250.0001.0000.0630.0000.0000.0000.0000.061
customer_gender0.0000.1010.0660.0320.0000.0440.0631.0000.0780.0650.0000.0000.028
customer_library_distance_km-0.0390.186-0.008-0.020-0.0020.0000.0000.0781.0000.0000.0420.0450.304
customer_occupation0.0000.0390.0000.0000.0330.0280.0000.0650.0001.0000.0490.0320.000
library_address0.0000.0000.0000.0000.0000.0600.0000.0000.0420.0491.0001.0000.000
library_name0.0000.0000.0000.0000.0000.0610.0000.0000.0450.0321.0001.0000.000
returned_late0.0000.9780.1080.0340.0000.0300.0610.0280.3040.0000.0000.0001.000

Missing values

2024-10-23T16:36:02.100465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-23T16:36:02.393475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

library_namelibrary_addresscustomer_gendercustomer_educationcustomer_occupationcustomer_agecustomer_age_groupcustomer_addresscustomer_library_distance_kmbook_titlebook_authorsbook_categoriesbook_pricebook_pagesbook_agebook_out_of_library_daysreturned_late
0Multnomah County Library Woodstock6008 SE 49th Ave, Portland, OR 97206femaleGraduate DegreeTech5335-604942 SE 28th Ave, Portland, OR 972022.153Indian Financial System 5EKhanUnknown416.9975212140
1Multnomah County Library205 NE Russell St, Portland, OR 97212maleGraduate DegreeEducation & Health5535-602821 NE Klickitat St, Portland, OR 972122.196Advertising ManagementC. L. Tyagi, Arun KumarAdvertising217.0079014621
2Multnomah County Library Kenton8226 N Denver Ave, Portland, OR 97217maleGraduate DegreeEducation & Health3318-356614 N Wilbur Ave, Portland, OR 972171.439Geostatistics for Natural Resources EvaluationPierre Goovaerts, Department of Civil and Environmental Engineering Pierre GoovaertsScience414.5056121250
3Multnomah County Library North Portland512 N Killingsworth St, Portland, OR 97217maleHigh SchoolBusiness & Finance4135-602617 NE 33rd Ave, Portland, OR 972123.936Highlights of Natural Resources ManagementUnited States. National Park ServiceConservation of natural resources149.0053024210
4Multnomah County Library205 NE Russell St, Portland, OR 97212femaleCollegeTech5035-605425 SE Lafayette St, Portland, OR 972066.624Some Vocational Resources and Needs of a Rural Community as Determined by the Location of Special GroupsEdwin Ray HoskinsAgricultural education432.5068494180
5Multnomah County Library Northwest2300 NW Thurman St, Portland, OR 97210femaleOthersBusiness & Finance4235-603103 SW Fairview Blvd, Portland, OR 972052.079Modern MedicineJohn Harvey KelloggBacteriology520.00724116100
6Multnomah County Library Holgate7905 SE Holgate Blvd, Portland, OR 97206femaleGraduate DegreeEducation & Health5535-608228 SE Division St, Portland, OR 972661.583Hand-book of Modern Steam Fire-enginesStephen RoperFire engines150.00368129631
7Multnomah County Library Gregory Heights7921 NE Sandy Blvd, Portland, OR 97213femaleOthersTech5235-605631 SE 28th Ave, Portland, OR 972028.925Development of non-bank financial institutions and capital markets in European union accession countriesMarie-Renée Bakker, Alexandra Gröss, World BankCapital market182.504161450
8Multnomah County Library Holgate7905 SE Holgate Blvd, Portland, OR 97206maleHigh SchoolSales6760+2610 SE 92nd Ave, Portland, OR 972661.794Natural and Energy ResourcesUnited States. Joint Chiefs of StaffUnknown481.006945080
9Multnomah County Library Capitol Hill10723 SW Capitol Hwy, Portland, OR 97219maleGraduate DegreeBlue Collar3018-3514230 SW Alpine Crest Way, OR 972247.720Resources and Opportunities of MontanaMontana. Department of Agriculture and PublicityAgriculture357.008099580
library_namelibrary_addresscustomer_gendercustomer_educationcustomer_occupationcustomer_agecustomer_age_groupcustomer_addresscustomer_library_distance_kmbook_titlebook_authorsbook_categoriesbook_pricebook_pagesbook_agebook_out_of_library_daysreturned_late
1476Multnomah County Library North Portland512 N Killingsworth St, Portland, OR 97217maleCollegeSales4335-606394 N Albina Ave, Portland, OR 972170.807English Mechanics and the World of ScienceUnknownIndustrial arts721.00925111110
1477Multnomah County Library Midland805 SE 122nd Ave, Portland, OR 97233femaleCollegeBusiness & Finance2518-3511889 SE Evergreen Hwy, Vancouver, WA 986839.285The Behavioral Sciences and Preventive Medicine Opportunities and DilemmasRobert L. KaneMedicine, Preventive142.0040841421
1478Multnomah County Library Central801 SW 10th Ave, Portland, OR 97205maleHigh SchoolOthers2918-354608 SW Northwood Ave, Portland, OR 972393.366Michigan Manufacturer & Financial RecordUnknownIndustries371.007511021061
1479Multnomah County Library Northwest2300 NW Thurman St, Portland, OR 97210femaleOthersUnknown8Under 181337 SW Upland Dr, Portland, OR 972213.256Tractor and Gas Engine ReviewUnknownInternal combustion engines557.99797105250
1480Multnomah County Library Hillsdale1525 SW Sunset Blvd, Portland, OR 97239maleGraduate DegreeOthers5435-609157 SW Woodside Dr, OR 972256.020Aircraft Propulsion and Gas Turbine EnginesAhmed F. El-SayedScience630.5093211071
1481Multnomah County Library St Johns7510 N Charleston Ave, Portland, OR 97203femaleOthersBlue Collar13Under 1811122 NW Saltzman Rd, Portland, OR 972294.617International Record of Medicine and General Practice ClinicsEdward Swift Dunster, Frank Pierce Foster, James Bradbridge Hunter, Charles Eucharist de Medicis Sajous, Gregory Stragnell, Henry J. Klaunberg, Félix Martí-IbáñezMedicine45.0012497841
1482Multnomah County Library Holgate7905 SE Holgate Blvd, Portland, OR 97206femaleHigh SchoolBlue Collar3935-603567 SE 92nd Ave, Portland, OR 972661.219International Banking and Financial CentersYoon Park, Yoon S. Park, Musa EssayyadBusiness & Economics276.50545291711
1483Multnomah County Library Hillsdale1525 SW Sunset Blvd, Portland, OR 97239femaleGraduate DegreeSales2418-353392 SW 78th Ave, OR 972255.286Elements of MechanicsGeorge William ParkerMechanics654.506021071651
1484Multnomah County Library Sellwood Moreland7860 SE 13th Ave, Portland, OR 97202femaleOthersSales6860+5900 SW 27th Ct, Portland, OR 972394.392Popular MechanicsUnknownUnknown281.99432661201
1485Multnomah County Library Capitol Hill10723 SW Capitol Hwy, Portland, OR 97219femaleHigh SchoolBlue Collar1918-3517271 SW Jurgens Ave, Tualatin, OR 970627.662Heavy Oil as Fuel for Internal-combustion EnginesIrving Cowan AllenInternal combustion engines55.993301051451